Efficient computational algorithm for optimal continuous experimental designs
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Pages (from-to) | 98-113 |
Journal / Publication | Journal of Computational and Applied Mathematics |
Volume | 350 |
Online published | 24 Oct 2018 |
Publication status | Published - Apr 2019 |
Externally published | Yes |
Link(s)
Abstract
A simple yet efficient computational algorithm for computing the continuous optimal experimental design for linear models is proposed. An alternative proof of the monotonic convergence for [Formula presented]-optimal criterion on continuous design spaces is provided. We further show that the proposed algorithm converges to the [Formula presented]-optimal design. We also provide an algorithm for the [Formula presented]-optimality and conjecture that the algorithm converges monotonically on continuous design spaces. Different numerical examples are used to demonstrate the usefulness and performance of the proposed algorithms.
Research Area(s)
- Continuous experimental design, Regression model, [Formula presented]-optimal
Citation Format(s)
Efficient computational algorithm for optimal continuous experimental designs. / Duan, Jiangtao; Gao, Wei; Ng, Hon Keung Tony.
In: Journal of Computational and Applied Mathematics, Vol. 350, 04.2019, p. 98-113.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review